Positioning in Urban Scenarios Based on Smartphone Camera/IMU and Digital TV Signal

Abstract The built-in sensors of smartphone, such as cameras, accelerometers and gyroscopes, provide abundant environmental information for positioning. VIO (visual-inertial odometry) can be derived from the embedded camera and inertial sensors to provide the estimation of a continuous and smooth trajectory of the pedestrian. However, the trajectory will drift due to the accumulation of system errors. Currently, DTV (digital TV) signal has been verified to be promising for precise ranging in urban scenarios. Therefore, this paper investigates a method of fusing the VIO with the ranging information from DTV signal to improve the positioning accuracy. An Extended Kalman filter is presented to estimate pedestrian mobile state. Field tests have been carried out and the fusion results can effectively reduce trajectory drift errors and improves positioning performance.